Liquid Cooling and AI Data Center Daily | 2026-07-05
A daily English mirror of liquid cooling, AI data center efficiency, research papers, products, policy, financing, and supply-chain signals.
1. Executive brief
This English edition mirrors the same public-source dataset used by the Chinese daily report for 2026-07-05.
- Collection window: 2026-07-04 08:00 北京时间 - 2026-07-05 08:00 北京时间.
- Coverage snapshot: 8 industry items; 8 technology items; 8 paper or white-paper items; 8 video signals.
- Current hot topics: 电力并网与能源约束, 智算中心 CapEx/扩建, NVIDIA Blackwell/GB200/GB300, AI 芯片供给与交付.
- The heat score is 10/10 and should be read as a source-density signal, not as an investment indicator.
All claims should be verified against the original source links listed at the end of this report.
Academic and Industry Briefs
Papers, videos, industry updates, policy, financing, and projects are compressed into scannable tags with a title, summary, and source link.
Academic
Research papers, methods, research-oriented videos, and academic signals.
Grid-Interactive Thermal Management of AI Data Centers via Contextual Dis…
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent ther…
Expand
Grid-Interactive Thermal Management of AI Data Centers via Contextual Distributionally Robust Optimization
- Published
- 2026-07-01
- Authors
- Jiachen Shen, Jian Shi, Yijie Yang, Chenye Wu, Dan Wang, Ju Bin Song, Zhu Han
- Theme
- 算电协同
Abstract
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent thermal violations, existing cooling strategies either enforce conservative, rigid bounds that severely limit grid responsiveness, or rely on forecast-driven controllers that perform poorly under AI workload uncertainty and distribution shifts. To overcome the above challenges, this paper proposes a Contextual Distributionally Robust Optimization (CDRO) framework for grid-interactive cooling control. Unlike standard DRO with fixed ambiguity sets, the proposed approach dynamically adapts the Wasserstein radius using real-time AI and grid context. This safely shrinks uncertainty bounds during stable regimes, unlocking deep demand-side flexibility. Theoretically, we formulate the control as an infinite-dimensional inf-sup problem, derive an exact tractable reformulation for the Wasserstein worst-case expected-cost term, and then derive a tractable conservative deterministic counterpart for the Distributionally Robust Conditional Value at Risk (DR-CVaR) thermal safety constraint. Solved via a scalable nested Alternating Direction Method of Multipliers (ADMM) algorithm, the CDRO controller achieves near-zero thermal violations under extreme workload spikes in high-fidelity EnergyPlus co-simulations. Simultaneously, it reduces the operational cost premium of robustness by approximately 13.7 percentage points relative to standard Min-Max Model Predictive Control (MPC).
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Jiachen Shen, Jian Shi, Yijie Yang, 等. Grid-Interactive Thermal Management of AI Data Centers via Contextual Distributionally Robust Optimization[J/OL]. (2026-07-01)[2026-07-05]. http://arxiv.org/abs/2607.00099v1.
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructu…
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debate…
Expand
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa
- Published
- 2026-06-24
- Authors
- Kai-Hsin Hung, Sumaya Nur Adan, Krupa Suchak, Armita Sadeghian Barzoki, Kofi Yeboah, Mohammad Amir Anwar
- Theme
- 热管理与液冷
Abstract
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute primarily as a technical input rather than as an outcome of investment, ownership, and financial control. This paper examines AI infrastructure investment flows across Africa through a systematic analysis of 46 publicly announced projects totalling USD $12.7 billion between 2019 and 2025. Using a value chain framework, we analyze who invests in AI-relevant infrastructure and where investments concentrate. Our findings reveal a highly concentrated landscape dominated by global data center operators, hyperscale technology firms, and development finance institutions, clustering in South Africa, Kenya, Nigeria, and Egypt. We introduce asymmetrical interdependence to describe a structural condition in which capital and physical infrastructure account for 73% of total funding while control remains concentrated in the compute layer among a small number of global technology firms. We argue that compute governance must account for capital flows, ownership, and control, not only geographic access, because these dynamics shape AI compute equity. Infrastructure presence is necessary but insufficient for meaningful governance capacity.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Kai-Hsin Hung, Sumaya Nur Adan, Krupa Suchak, 等. Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa[J/OL]. (2026-06-24)[2026-07-05]. http://arxiv.org/abs/2606.28404v1.
Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable …
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operatio…
Expand
Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters
- Published
- 2026-06-23
- Authors
- Shuyi Chen, Zhengchang Hua, Nikos Tziritas, Georgios Theodoropoulos
- Theme
- AI 运维优化
Abstract
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operational carbon" alternative. However, the sub-$10μ\text{s}$ communication latency required for synchronized scientific workloads, such as distributed Large Language Model (LLM) training, forces ODCs into extreme physical density, triggering a critical "Proximity-Thermal Paradox." As these high-density systems scale into Monolithic Structures or Proximity Swarms, they suffer from intense thermal-fluid crosstalk (heat traps in shared cooling loops) and thermal-radiative crosstalk (mutual heating that blocks deep-space cooling radiators). If left unmitigated, this persistent heat stagnation not only triggers severe thermal throttling that degrades training throughput, but also induces severe thermal fatigue, drastically shortening hardware lifespans and generating premature space e-waste. To make orbital AI truly sustainable, this position paper challenges traditional uniform load-sharing. We propose the Thermal-Aware Heterogeneity Thesis, which treats spatial cooling variances as a primary resource management dimension. Building on this, we introduce Thermal-Load Balancing (TLB), a software framework that dynamically migrates these intensive workloads to the coolest available units based on instantaneous fluid temperatures or absorbed radiation. Our analysis demonstrates that TLB resolves thermal bottlenecks to restore Model Flops Utilization (MFU), while simultaneously reducing physical thermal stress. Extending the operational lifespan of orbital hardware is crucial to amortize the massive embodied carbon of rocket launches, outlining a necessary pathway to scale orbital AI without accelerating e-waste.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Shuyi Chen, Zhengchang Hua, Nikos Tziritas, 等. Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters[J/OL]. (2026-06-23)[2026-07-05]. http://arxiv.org/abs/2606.26150v2.
A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbal…
This paper proposes a grid-aware coordination framework between data centers and distribution grids using a DLMP-based bilevel opti…
Expand
A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbalanced Three-Phase Distribution Systems
- Published
- 2026-06-25
- Authors
- Arash Baharvandi, Duong Tung Nguyen
- Theme
- 算电协同
Abstract
This paper proposes a grid-aware coordination framework between data centers and distribution grids using a DLMP-based bilevel optimization model. The data center aggregator (DCA) determines active power demand in response to distribution locational marginal prices (DLMPs), while the distribution system operator (DSO) solves a network-constrained optimal power flow problem to determine DLMPs in an unbalanced three-phase system. The model incorporates both active and reactive power consumption of data centers to evaluate their impacts on voltage regulation and phase imbalance. To mitigate adverse network effects, two operating cases are analyzed: without reactive power compensation and with static var generator (SVG)-based compensation. The proposed approach is validated on the IEEE 37-bus unbalanced distribution test system. Simulation results show that DLMP-based coordination captures economically efficient data center operation, and phase- and location-dependent network conditions, while SVG-based compensation improves voltage profiles and reduces phase unbalance.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Arash Baharvandi, Duong Tung Nguyen. A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbalanced Three-Phase Distribution Systems[J/OL]. (2026-06-25)[2026-07-05]. http://arxiv.org/abs/2606.26328v1.
AI Data Centers and the Water Use Feedback Loop
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. Thes…
Expand
AI Data Centers and the Water Use Feedback Loop
- Published
- 2026-06-20
- Authors
- Basit A. Akinade, Amobichukwu C. Amanambu, Jonathan M. Frame, Shaolei Ren
- Theme
- 热管理与液冷
Abstract
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. These dynamics are studied separately yet form a feedback loop. This review formalises the Water and AI Feedback Loop, introduces the Water Consumption Impact index to quantify community-scale utility burden, and demonstrates across ten US sites that burden spans three orders of magnitude, from 0.2% to 134% of host capacity.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Basit A. Akinade, Amobichukwu C. Amanambu, Jonathan M. Frame, 等. AI Data Centers and the Water Use Feedback Loop[J/OL]. (2026-06-20)[2026-07-05]. http://arxiv.org/abs/2606.21760v1.
GaN Power Devices and Converter Architectures for AI Data Centers: Effici…
The growth of artificial-intelligence workloads is increasing the electrical and thermal demands on data-center power-delivery syst…
Expand
GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways
- Published
- 2026-06-24
- Authors
- Donald Intal, Abasifreke Ebong
- Theme
- 算电协同
Abstract
The growth of artificial-intelligence workloads is increasing the electrical and thermal demands on data-center power-delivery systems, making conversion efficiency, power density, and reliability critical design priorities. This review examines how gallium-nitride (GaN) power devices can be matched to specific stages of the grid-to-load conversion chain, including power-factor correction, isolated DC/DC conversion, 48-V intermediate-bus conversion, and point-of-load regulation. Si, SiC, and GaN are compared using converter-relevant metrics, and lateral, vertical, and specialized GaN architectures are evaluated in terms of voltage scalability, switching behavior, reverse conduction, thermal pathways, gate control, and technology maturity. The analysis shows that GaN provides a stage-dependent rather than universal advantage. Commercial lateral GaN HEMTs are particularly effective in high-frequency, low-to-mid-voltage stages, while specialized and hybrid devices support bidirectional operation, normally-off control, extreme conversion ratios, and integration. Vertical GaN remains an emerging option for higher-voltage and higher-power conversion. A quantitative framework links cascaded converter efficiency to electrical-loss reduction, cooling demand, annual facility energy use, and operational carbon emissions. Broad deployment further requires low-parasitic packaging, disciplined gate-drive and EMI co-design, mission-profile reliability qualification, scalable manufacturing, and supply-chain resilience. GaN is therefore best treated as a stage-specific system lever whose value depends on coordinated device, topology, package, and thermal co-design.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Donald Intal, Abasifreke Ebong. GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways[J/OL]. (2026-06-24)[2026-07-05]. http://arxiv.org/abs/2606.25281v1.
From Tokens to Energy Flexibility: Quantization-Enabled Demand Response f…
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-mana…
Expand
From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads
- Published
- 2026-06-17
- Authors
- Bojun Du, Xiaoyi Fan, Ershun Du, Long Chen, Jianpei Han, Qingchun Hou, Ning Zhang, Chongqing Kang
- Theme
- 算电协同
Abstract
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data-center energy management mainly relies on temporal and spatial workload shifting and campus-level energy asset scheduling, but it usually treats LLM inference demand as an aggregate load. As a result, these approaches fail to exploit the internal characteristics of LLM serving and therefore overlook the flexibility offered by LLM-specific techniques such as model quantization. To unlock this flexibility, this paper proposes a quantization-enabled energy management framework for grid-responsive LLM inference data centers. First, a quantization-to-power model is established to map each model--quantization configuration to a compact set of dispatchable parameters. Second, a two-stage quantization-enabled DR model is developed to account for model instance switching, request routing, and precision selection. Third, a multi-campus co-optimization method is introduced for DR participation by integrating grid-side electricity and carbon signals with the quantization-enabled DR model. Case studies show that the proposed framework reduces total data-center operating cost by 34.3\% without curtailing served token volume, validating model quantization as an effective flexibility lever for grid-responsive LLM data-center energy management.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Bojun Du, Xiaoyi Fan, Ershun Du, 等. From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads[J/OL]. (2026-06-17)[2026-07-05]. http://arxiv.org/abs/2606.18851v1.
Spatial Load Correlation in AI Data-Center-Dominated Power Systems
The proliferation of large-scale data centers introduces spatially correlated demand profiles that challenge the long-standing assu…
Expand
Spatial Load Correlation in AI Data-Center-Dominated Power Systems
- Published
- 2026-06-12
- Authors
- Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, Mohammed Benidris, Joydeep Mitra
- Theme
- 算电协同
Abstract
The proliferation of large-scale data centers introduces spatially correlated demand profiles that challenge the long-standing assumption of statistical independence of loads in power system analysis. This paper examines the emergence of such load correlations and evaluates their impact on data-center-dominated grids. Analytical derivations reveal that correlated load fluctuations amplify aggregate stochastic disturbances, reduce voltage stability margins through weakened reactive power stiffness, and degrade frequency stability margin by erosion of natural load diversity effects. Real-time digital simulation studies confirm that moderate spatial correlation in distributed data centers produces simultaneous frequency deviations and voltage fluctuations across multiple buses. The findings offer transmission system operators a physics-based perspective to interpret emerging oscillatory phenomena and establish stability planning criteria grounded in measurable load-correlation structures rather than traditional diversity assumptions.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, 等. Spatial Load Correlation in AI Data-Center-Dominated Power Systems[J/OL]. (2026-06-12)[2026-07-05]. http://arxiv.org/abs/2606.13853v1.
The First US SMR Just Died — And It Took $1.4B With It ☢️
Compute & Concrete · Query: AI datacenter power grid university lecture。Useful as technical or research context.
Expand
Collective Energy-Efficiency Approach to Data Center Networks Planning
MyProjectBazaar · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
Expand
DLS with Keren Bergmann: Scaling Energy-Efficient AI Systems Performance …
MPI for the Science of Light · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
Expand
Energy-Efficient Management of Virtual Machines in Data Centers for Cloud …
Anton Beloglazov · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
Expand
Enhanced geothermal for AI data centers: Devilish or divine? | James F. G…
TEDx Talks · Query: AI data center energy conference keynote。Useful as technical or research context.
Expand
Panel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data …
W.Media- South Asia & Middle East · Query: data center liquid cooling conference presentation。Useful as technical or research conte…
Expand
电力并网与能源约束
Same-source item from the Chinese report. Verify details against the original linked source: 电力并网与能源约束
展开全文
智算中心 CapEx/扩建
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心 CapEx/扩建
展开全文
NVIDIA Blackwell/GB200/GB300
Same-source item from the Chinese report. Verify details against the original linked source: NVIDIA Blackwell/GB200/GB300
Expand
Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：How NVIDIA’s Inference Software Stack…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Ho…
展开全文
AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Claude Meets Blackwell Ultra: Anthrop…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Cl…
展开全文
智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 300MW（原文标题：300MW data center…
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Equinix-occupied data center in…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道（原…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $1.75 billion、10GW（原文标题：CPP Inves…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道，涉…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：New Jersey lawmakers pass bill …
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道（原…
展开全文
电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 $1.75bn、2GW（原文标题：National Grid V…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Dynamics 发布相关报道，…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 18MW（原文标题：Nebius signs 18MW lease…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道，涉…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Texas Governor Abbott calls for…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道（原…
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Amazon's carbon emissions grow …
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道（原…
展开全文
液冷与热管理进展：Data Center Dynamics 发布相关报道（原文标题：German chemicals firm Wacker la…
Same-source item from the Chinese report. Verify details against the original linked source: 液冷与热管理进展：Data Center Dynamics 发布相关报道（原…
展开全文
AI 算力基础设施动态：The Register 发布相关报道，涉及 10 GW（原文标题：SoftBank enters the rent-a-…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：The Register 发布相关报道，涉及 10 …
展开全文
AI 算力基础设施动态：The Register 发布相关报道，涉及 $42 million（原文标题：Trouble keeps finding…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：The Register 发布相关报道，涉及 $42…
展开全文
技术与产品进展：ServeTheHome 发布相关报道，涉及 2026 w（原文标题：ASRock Rack Had One of the Fir…
Same-source item from the Chinese report. Verify details against the original linked source: 技术与产品进展：ServeTheHome 发布相关报道，涉及 2026 w（…
展开全文
技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：Rack-Based Environmental Monito…
Same-source item from the Chinese report. Verify details against the original linked source: 技术与产品进展：Data Center Knowledge 发布相关报道（原…
展开全文
智算中心/数据中心建设进展：Data Center Knowledge 发布相关报道（原文标题：NERC Flags AI Data Center…
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Knowledge 发布…
展开全文
AI 算力基础设施动态：The Register 发布相关报道（原文标题：Nvidia floats double-dipping datacen…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：The Register 发布相关报道（原文标题：N…
展开全文
电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Stargate Update: AI’s Biggest…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
展开全文
AI 算力基础设施动态：HPCwire 发布相关报道，涉及 $35、$35 million、$60 million（原文标题：OXMIQ Rais…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：HPCwire 发布相关报道，涉及 $35、$35 …
展开全文
[WEBINAR] ASHRAE's 5th Edition of Thermal Guidelines: What's New and How …
Upsite Technologies · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
Expand
Major Changes to ASHRAE’s Fifth Edition of Thermal Guidelines: New Air-Co…
Upsite Technologies · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
Expand
产业热度指数 10/10
Same-source item from the Chinese report. Verify details against the original linked source: 产业热度指数 10/10
展开全文
4. Video signals
The First US SMR Just Died — And It Took $1.4B With It ☢️
专家讲座 · Compute & Concrete · Query: AI datacenter power grid university lecture
Open on YouTube[WEBINAR] ASHRAE's 5th Edition of Thermal Guidelines: What's New and How It Can Impact Your Facility
标准组织讲座 · Upsite Technologies · Query: ASHRAE data center cooling webinar
Open on YouTubeCollective Energy-Efficiency Approach to Data Center Networks Planning
学术讲座 · MyProjectBazaar · Query: IEEE data center energy efficiency lecture
Open on YouTubeDLS with Keren Bergmann: Scaling Energy-Efficient AI Systems Performance with Photonic Connectivity
学术讲座 · MPI for the Science of Light · Query: IEEE data center energy efficiency lecture
Open on YouTubeEnergy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing
学术讲座 · Anton Beloglazov · Query: IEEE data center energy efficiency lecture
Open on YouTubeEnhanced geothermal for AI data centers: Devilish or divine? | James F. Groves | TEDxChantilly HS
学术会议报告 · TEDx Talks · Query: AI data center energy conference keynote
Open on YouTubeMajor Changes to ASHRAE’s Fifth Edition of Thermal Guidelines: New Air-Cooled Class for High Density
标准组织讲座 · Upsite Technologies · Query: ASHRAE data center cooling webinar
Open on YouTubePanel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data Centers
学术会议报告 · W.Media- South Asia & Middle East · Query: data center liquid cooling conference presentation
Open on YouTubeSources
Collection notes
- 论文池：已从本地论文池读取 20 条候选；池更新时间 2026-07-05 15:04。
- When optional automation services are unavailable, this page uses traceable public sources and conservative rule-based summaries only; unverifiable facts are not filled in.